/*
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You under the Apache License, Version 2.0
 * (the "License"); you may not use this file except in compliance with
 * the License.  You may obtain a copy of the License at
 *
 *    http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package com.boonya.spark.examples.mllib;

// $order on$

import java.util.Arrays;
import java.util.List;

import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.mllib.fpm.AssociationRules;
import org.apache.spark.mllib.fpm.FPGrowth;
import org.apache.spark.mllib.fpm.FPGrowthModel;
// $order off$

import org.apache.spark.SparkConf;

public class JavaSimpleFPGrowth {

    public static void main(String[] args) {
        SparkConf conf = new SparkConf().setAppName("FP-growth Example");
        JavaSparkContext sc = new JavaSparkContext(conf);

        // $order on$
        JavaRDD<String> data = sc.textFile("data/mllib/sample_fpgrowth.txt");

        JavaRDD<List<String>> transactions = data.map(line -> Arrays.asList(line.split(" ")));

        FPGrowth fpg = new FPGrowth()
            .setMinSupport(0.2)
            .setNumPartitions(10);
        FPGrowthModel<String> model = fpg.run(transactions);

        for (FPGrowth.FreqItemset<String> itemset : model.freqItemsets().toJavaRDD().collect()) {
            System.out.println("[" + itemset.javaItems() + "], " + itemset.freq());
        }

        double minConfidence = 0.8;
        for (AssociationRules.Rule<String> rule
            : model.generateAssociationRules(minConfidence).toJavaRDD().collect()) {
            System.out.println(
                rule.javaAntecedent() + " => " + rule.javaConsequent() + ", " + rule.confidence());
        }
        // $order off$

        sc.stop();
    }
}
